Chromatin immunoprecipitation and sequencing (ChIP-seq) can be used to map histone modifications genome-wide, enabling the identification of cell-type-specific functional genomic elements and epigenetic states. However, this technology has several limitations. Recent adaptations of the method can address individual limitations, but there are tradeoffs and limitations to each of these approaches.
First, conventional ChIP-seq procedures involve separate immunoprecipitations (IPs) that are sensitive to the amount of chromatin input and the quality of the antibody. This compromises the accuracy with which chromatin landscapes can be quantitatively compared across samples. The lack of quantitative information in ChIP-seq data is a long-standing problem and can obscure global differences in histone modification levels due to cell state transitions or genetic mutations in epigenetic regulators frequently observed in cancer (Ryan and Bernstein, 2012). Recent studies have presented strategies for quantitatively comparing ChIP-seq signal intensities by incorporating exogenous DNA or synthetic histone spike-in controls, but these protocols may not be compatible with low cell numbers and/or aneuploidy (Bonhoure et al., 2014; Grzybowski et al., 2015; Orlando et al., 2014). Quantitative consistency might also be achieved by combining samples in the same reaction; therefore developing a method for processing many samples in the same ChIP-seq assay would be valuable.
Second, conventional ChIP-seq experiments require large numbers of cells. Progress has been made toward reducing cell requirements, but the corresponding methods do not address the challenge of quantitative comparison, are low throughput, and/or have only been demonstrated for certain modifications (Adli et al., 2010; Brind'Amour et al., 2015; Gilfillan et al., 2012; Lara-Astiaso et al., 2014). Limitations have been addressed by (1) minimizing loss and (2) performing linear amplification of input material, which have individually been demonstrated to increase sensitivity of ChIP experiments (O'Neill et al., 2006; Shankaranarayanan et al., 2011).
Third, the throughput of ChIP-seq is constrained by individual sample processing. Progress has been made toward high-throughput ChIP, but these methods have other limitations such as high input requirements (Chabbert et al., 2015; Garber et al., 2012). Accordingly, what is also needed are methods that increase sample processing throughput.